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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Mahmoud, S.A. |
| Copyright Year | 2008 |
| Description | Author affiliation: Inf. & Comput. Sci., King Fahd Univ. of Pet. & Miner., Dhahran (Mahmoud, S.A.) |
| Abstract | Arabic (Indian) handwritten digits recognition is useful in a large variety of banking and business applications and in postal zip code reading, and data entry applications. In this paper we present a technique for the automatic recognition of Arabic (Indian) handwritten digits using Gabor-based features and support vector machines (SVMs). A database consisting of 21120 samples written by 44 writers is used. 70% of the data is used for training and the remaining 30% is used for testing. Several scales and orientations are used to extract the Gaborbased features. The achieved average recognition rates are 99.85% and 97.94% using 3 scales & 5 orientations and using 4 scales & 6 orientations, respectively. The experimental results indicate the effectiveness of the Gabor-based features and SVM for Arabic (Indian) digits recognition. |
| Starting Page | 683 |
| Ending Page | 687 |
| File Size | 348908 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424433964 |
| DOI | 10.1109/INNOVATIONS.2008.4781779 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-12-16 |
| Publisher Place | United Arab Emirates |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Banking Application software Support Vector Machines Petroleum Support vector machines Computer science Handwriting recognition Image segmentation Recognition of handwritten Arabic numerals Hidden Markov models Feature extraction Gabor filters Arabic (Indian) digits |
| Content Type | Text |
| Resource Type | Article |
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